Method and devices for performing cardiac waveform appraisal

ABSTRACT

Implementations of various technologies described herein are directed toward a sensing architecture for use in cardiac rhythm management devices. The sensing architecture may provide a method and means for certifying detected events by the cardiac rhythm management device. Moreover, by exploiting the enhanced capability to accurately identifying only those sensed events that are desirable, and preventing the use of events marked as suspect, the sensing architecture can better discriminate between rhythms appropriate for device therapy and those that are not.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/189,386, filed Jul. 22, 2011, now U.S. Pat. No. 8,185,198 publishedas US Patent Application Publication Number 2011-0282406, which is acontinuation of U.S. patent application Ser. No. 11/781,687, filed Jul.23, 2007, now U.S. Pat. No. 7,996,082; which is a divisional of U.S.patent application Ser. No. 10/858,598, filed Jun. 1, 2004 and now U.S.Pat. No. 7,248,921; which claims the benefit of and priority to U.S.Provisional Patent Application Ser. No. 60/475,279, filed Jun. 2, 2003.Each of the aforementioned related patent applications is hereinincorporated by reference.

FIELD

Implementations of various technologies described herein are related tothe field of implantable cardiac treatment devices, and moreparticularly, to methods of electrically sensing cardiac events andconfirming the accuracy in detecting a cardiac event prior todetermining whether treatment is needed.

DESCRIPTION OF RELATED ART

The following descriptions and examples do not constitute an admissionas prior art by virtue of their inclusion within this section.

Implantable cardiac rhythm management devices are an effective treatmentin managing irregular cardiac rhythms in particular patients.Implantable cardiac rhythm management devices are capable of recognizingand treating arrhythmias with a variety of therapies. These therapiesrange from providing anti-bradycardia pacing for treating bradycardia,anti-tachycardia pacing or cardioversion energy for treating ventriculartachycardia, to high energy shock for treating ventricular fibrillation.Frequently, the cardiac rhythm management device delivers thesetherapies for the treatment of tachyarrhythmias in sequence; startingwith anti-tachycardia pacing and then proceeding to low energy shocks,and then, finally, to high energy shocks. Sometimes, however, only oneof these therapies is selected depending upon the tachyarrhythmiadetected.

To effectively deliver these treatments, cardiac rhythm managementdevices must first accurately detect and classify an episode. Throughaccurate determination and quantification of sensed cardiac events,these cardiac rhythm management devices are able to classify the type ofarrhythmia that is occurring and assess the appropriate therapy toprovide to the heart, if any. A problem arises, however, when thecardiac rhythm management device senses noise, and mistakenly declaresan episode. As a result, in particular instances, the cardiac rhythmmanagement device may inappropriately deliver therapy.

Extra-cardiac noise may cause a cardiac rhythm management device tomisclassify noise events as a tachyarrhythmia. In illustration, byincorporating skeletal muscle noise artifact, or other noise, into acardiac rate calculation, the cardiac rhythm management device mightinaccurately calculate the ventricular rate as one that is elevated. Ifthe ventricular rate is mistakenly calculated to be elevated over athreshold rate boundary, a frequent determiner of tachyarrhythmias, thecardiac rhythm management device may inappropriately deliver therapy toa patient.

Additionally, problems arise when the cardiac therapy device withholdstherapy after mischaracterizing a sensed event. For example,anti-bradycardia devices deliver a pacing pulse based on whether acardiac event is sensed within a particular time frame. If the sensingarchitecture fails to sense a cardiac event within a preset time period,the cardiac rhythm management device will deliver a pacing pulse to theheart. This pacing pulse is timed in a preset sequence to induce thepatient's heart to contract in a proper rhythm. This therapy, however,may be compromised by having the cardiac rhythm management device senseand characterize an extraneous event as a “true” cardiac event. If thesensing architecture erroneously classifies noise (such as skeletalmuscle artifact or other noise) as a “true” cardiac event, then a pacingpulse may be incorrectly withheld. This is particularly problematic whena pacing pulse is required to maintain a physiologically necessary rateof the patient's heart.

Besides being noticeable and sometimes physically painful to thepatient, when a cardiac rhythm management device delivers inappropriatetreatment, it can be extremely disconcerting to the patient. Moreover,delivery of an inappropriate therapy can intensify the malignancy of thecardiac arrhythmia. Therefore, the accuracy of a sensing architecture isan important factor in ensuring that appropriate therapy is delivered toa patient.

Current implantable cardiac rhythm management devices incorporate asensing architecture that detects likely cardiac events and renders adecision regardless of the accuracy of those originally detected events.As such, current implantable cardiac rhythm management devices mustinclude painstakingly designed sensing architectures to try and avoiderroneous detections. Prior art devices have been developed withrudimentary systems and methods in an attempt to determine whether noiseis present on a sampled cardiac signal. If noise is detected in thesedevices, the manner in which the cardiac signal is acquired, or themanner in which the device operates in response to the acquired signal,is altered. This reduces the impact of erroneously detecting noise and,therefore, inappropriately triggering or withholding therapy. Thismethodology, however, leaves the cardiac rhythm management device opento significant sensing drawbacks, one of which is that it continuallyperturbates the sensing architecture.

Certain prior art implantable cardiac rhythm management devicescontinuously adjust parameters such as amplifier gain in response toextra-cardiac noise, which allows for the possibility that the sensingarchitecture may miss cardiac events. When adjusting the gain control tolessen sensitivity by raising the sensing floor to avoid noise, it ispossible to miss actual cardiac events especially during polymorphicrhythms including ventricular fibrillation. In particular, the sensingarchitecture may miss discrete cardiac beats, or otherwise stated, misstrue positives. By missing a cardiac event, rhythm and beat sensitivityis diminished.

Other implantable cardiac rhythm management devices in the prior artrepeatedly extend a noise window during continuous noise. When thesewindow extensions either reach a specific number, or more commonly reachthe end of a predetermined interval, the device reverts to a non-sensingor asynchronous behavior for a limited period of time. This type ofreversion behavior can miss a cardiac event, therefore reducing rhythmand beat sensitivity. Additionally, these reversion approaches to noiseare generally only useful for continuous noise. Noise is most frequentlyburst in nature, for which most reversion schemes are not effective.This often results in overdetection and a potential for inappropriatetherapy. Prior art cardiac rhythm management devices frequently utilizethese methodologies contiguously.

SUMMARY

Implementations of various technologies described herein are directedtoward a sensing architecture for use in cardiac rhythm managementdevices. The sensing architecture provides a method and means forcertifying detected events by the cardiac rhythm management device.Moreover, by exploiting the enhanced capability for accuratelyidentifying and using information from only those sensed events that arecertified, the sensing architecture can better discriminate betweenrhythms appropriate for device therapy and those that are not.

Implementations of various technologies described herein are alsodirected toward a method of signal detection enhancement for a cardiacrhythm device comprising receiving a signal from electrodes implantedfor cardiac observation, observing characteristic features of thesignal, counting the characteristic features, and comparing the numberof characteristic features to a threshold either to certify the signalfor use in characterizing a cardiac complex, or to determine the signalis unsuitable for use in characterizing a cardiac complex. In someimplementations, the characteristic features may include a number ofsignificant maximum slope points in the sensed signal. In otherimplementations, the characteristic features may include a number ofmonotonic segments in the sensed signal, or may include a number ofsample groups within the sensed signal that are monotonic. Additionalimplementations may include systems and devices suited for performingsuch methods.

The above referenced summary section is provided to introduce aselection of concepts in a simplified form that are further describedbelow in the detailed description section. The summary is not intendedto identify key features or essential features of the claimed subjectmatter, nor is it intended to be used to limit the scope of the claimedsubject matter. Furthermore, the claimed subject matter is not limitedto implementations that solve any or all disadvantages noted in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of various technologies will hereafter be described withreference to the accompanying drawings. It should be understood,however, that the accompanying drawings illustrate only the variousimplementations described herein and are not meant to limit the scope ofvarious technologies described herein.

FIGS. 1A-1B illustrate, respectively, representative subcutaneous andintravenous ICD systems in connection with implementations of varioustechnologies described herein.

FIG. 2 illustrates a block diagram of a sensing architecture inaccordance with implementations of various technologies describedherein.

FIG. 3 shows an electrocardiogram having a plurality of certifiedcardiac complexes used in classification and a plurality of cardiaccomplexes being marked suspect by the sensing architecture and not usedin classification in accordance with implementations of varioustechnologies described herein.

FIG. 4 shows an electrocardiogram sample having no waveform appraisalphase implemented by the sensing architecture in accordance withimplementations of various technologies described herein.

FIG. 5 shows the same electrocardiogram sample as that depicted in FIG.3, but with the waveform appraisal phase implemented in accordance withimplementations of various technologies described herein.

FIG. 6 shows a block diagram illustrating the steps for waveformappraisal in accordance with one implementation of various technologiesdescribed herein.

FIG. 7 shows a block diagram illustrating the steps employed forwaveform appraisal in accordance with another implementation of varioustechnologies described herein.

FIG. 8 shows a block diagram illustrating the steps employed forwaveform appraisal in accordance with yet another implementation ofvarious technologies described herein.

FIGS. 9A-9B illustrate, graphically, operation of the waveform appraisalmethods of FIGS. 7 and 8 on a clean QRS signal in accordance withimplementations of various technologies described herein.

FIGS. 10A-10B illustrate, graphically, operation of the waveformappraisal methods of FIGS. 7 and 8 on a noisy signal that is notsuitable for beat classification in accordance with implementations ofvarious technologies described herein.

FIG. 11 illustrates, graphically, operation of another waveformappraisal method further illustrated in FIG. 12 in accordance withimplementations of various technologies described herein.

FIG. 12 shows a block diagram illustrating the steps employed in amaximum slope point method of waveform appraisal in accordance withimplementations of various technologies described herein.

DETAILED DESCRIPTION

The discussion below is directed to certain specific implementations. Itis to be understood that the discussion below is only for the purpose ofenabling a person with ordinary skill in the art to make and use anysubject matter defined now or later by the patent “claims” found in anyissued patent herein.

Implementations of various technologies are generally related to cardiacrhythm management devices (e.g., an ImplantableCardioverter/Defibrillator (ICD) system) that provide therapy forpatients experiencing particular arrhythmias. Implementations of varioustechnologies are directed toward sensing architectures for use incardiac rhythm management devices. In particular, implementations ofvarious technologies are suited for ICD systems capable of detecting anddefibrillating harmful arrhythmias. Although the sensing architecture isintended primarily for use in an implantable medical device thatprovides defibrillation therapy, various implementations are alsoapplicable to cardiac rhythm management devices directed towardanti-tachyarrhythmia pacing (ATP) therapy, pacing, and other cardiacrhythm devices capable of performing a combination of therapies to treatrhythm disorders, including external devices.

To date, ICD systems have been epicardial systems or transvenous systemsimplanted generally as shown in FIG. 1B. However, as further explainedherein, various implementations are also adapted to function with asubcutaneous ICD system as shown in FIG. 1A.

FIG. 1A illustrates a subcutaneously placed ICD system. In thisimplementation, the heart 1 is monitored using a canister 2 coupled to alead system 3. The canister 2 may include an electrode 4 thereon, whilethe lead system 3 connects to sensing electrodes 5, 6, and a coilelectrode 7 that may serve as a shock or stimulus delivery electrode aswell as a sensing electrode. The various electrodes define a number ofsensing vectors V1, V2, V3, V4. It can be seen that each vector providesa different vector “view” of the heart's 1 electrical activity. Thesystem may be implanted subcutaneously as illustrated, for example, inU.S. Pat. Nos. 6,647,292 and 6,721,597, the disclosures of which areboth incorporated herein by reference. By subcutaneous placement, it ismeant that electrode placement does not require insertion of anelectrode into a heart chamber, the heart muscle, or the patient'svasculature.

FIG. 1B illustrates a transvenous ICD system. The heart 10 is monitoredand treated by a system including a canister 11 coupled to a lead system12 including atrial electrodes 13 and ventricular electrodes 14. Anumber of configurations for the electrodes may be used, includingplacement within the heart, adherence to the heart, or dispositionwithin the patient's vasculature. For example, Olson et al., in U.S.Pat. No. 6,731,978, illustrate electrodes disposed in each chamber ofthe heart for sensing, as well as shocking electrodes in addition to thesensing electrodes.

Various implementations may also be embodied by operational circuitryincluding select electrical components provided within the canister 2(FIG. 1A) or canister 11 (FIG. 1B). In such implementations, theoperational circuitry may be configured to enable the methods to beperformed. In some similar implementations, various technologiesdescribed herein may be embodied in readable instruction sets such as aprogram encoded in machine or controller readable media, wherein thereadable instruction sets are provided to enable the operationalcircuitry to perform the analysis discussed in the above referencedimplementations. Further implementations may include a controller ormicrocontroller adapted to read and execute the above methods.

FIG. 2 illustrates a sensing architecture 20 in accordance withimplementations of various technologies described herein. The sensingarchitecture 20 is separated into three distinct and autonomous phases.The three phases are (1) the detection phase 21, (2) the waveformappraisal phase 22 and (3) the classification phase 23. Decisions aremade in each of the three phases. Moreover, decisions made in each phasemay affect the decision-making process in subsequent phases. However, itis not necessarily the case that decisions made in an individual phasewill affect the decision-making process in preceding phases. Inillustration, a decision made in the waveform appraisal phase 22 mayaffect the decision-making process in the classification phase 23, butit may have no effect on the detection phase 21, or in any futuredecisions made in the detection phase 21.

The first phase of the sensing architecture 20 is the detection phase21. Within the detection phase 21 of the sensing architecture 20, datais collected by a cardiac rhythm management device. The manner in whichthe data is collected and the type of data collected is dependent on thecardiac rhythm management device being used. Moreover, the cardiacrhythm management device can be programmed to, or may automaticallyadapt to, optimally detect a particular form of data which is sought bythe cardiac rhythm management device. In a subcutaneous ICD system,subcutaneous electrodes are used to detect cardiac signals emitted fromthe patient's heart.

Once the raw detected signal data is received by the cardiac rhythmmanagement device, the detected data is then preprocessed, if requiredor desired. Preprocessing steps may include smoothing of the detecteddata, differentiation, filtering and other preprocessing methodologiesknown in the art. Finally, the detected data, whether preprocessed orraw, is initially classified as being either an event or not anevent—indicated in the block diagram at 24. More specifically, adetermination is made that an event was detected by the sensingarchitecture 20. An illustrative determination that an event wasdetected may include, for example, a determination that a signal hasbeen received having at least a certain amplitude likely indicating anR-wave from a cardiac complex or noise. The result is that the detectionphase 21 provides a sensed event to the waveform appraisal phase 22.

Following the detection phase 21 of the sensing architecture 20, sensedevents are appraised in the second phase, the waveform appraisal phase22. In the illustrative implementation, the waveform appraisal phase 22is a separate and independent phase in the sensing architecture 20. Itis within the waveform appraisal phase 22 where an analysis is performedon the sensed event 24 recognized in the detection phase 21 of thesensing architecture 20. In the waveform appraisal phase 22, anoperation is performed on the sensed event. More specifically, theappraisal operator 25 evaluates and certifies that what is sensed duringthe detection phase 21 is a high quality sensed event. A high qualitysensed event is a sensed event that can be used in classifying a cardiacrhythm, such as a sensed event that closely represents a cardiac “beat”without excessive noise. In contrast, a low quality event may be a noisesignal that does not represent the desired cardiac signal, or mayrepresent a sensed cardiac beat but the beat is superimposed with anoise artifact sufficient to render the sensed event unsuitable forclassification.

In one implementation, the sensed event being certified in the waveformappraisal phase 22 is the detection of a cardiac ventriculardepolarization. In the art, a cardiac ventricular depolarization isoften referred to as a QRS complex or R-wave. In this implementation,the waveform appraisal phase 22 evaluates and certifies that the sensedevent is a high quality R-wave that can be used for further decisionmaking. In some implementations, the events being certified may be thedetection of a P-wave (cardiac atrial depolarization), T-wave (cardiacventricular repolarization), a pacing artifact, or any other sensedsignal that may be utilized within a rhythm classification architecture.Various technologies described herein may also evaluate whether thesensed event was not an R-wave, P-wave, T-wave, pacing artifact, or anyother sensed signal that could be misidentified as the sensed event ofparticular interest.

In particularly noisy conditions, certain noise may appear as a cardiacevent, and thus be mistakenly sensed as such. Examples of noise that maycreate a low quality electrocardiogram signal include extra-cardiac(skeletal) muscle artifact, 50/60 Hertz interference, electromagneticinterference, electrocautery, or any other passing or intermittentelectrical occurrence.

Assuming that the detection phase 21 does sense noise as an event, thissensed event is then processed through the waveform appraisal phase 22so that the sensed event may be certified. For the illustrativeimplementation, at least some, but preferably all sensed events areprocessed through the waveform appraisal phase 22. In the waveformappraisal phase 22, the appraisal operator 25 examines the sensed eventthrough various methods and procedures (described below). In thisexample, noise may diminish the quality of the sensed event. Thus, thesensed event would be determined by the appraisal operator 25 to besomething other than a certifiable event. A non-certifiable event is onethat is “suspect”. Once the appraisal operator 25 has determined thatthe sensed event cannot be a certifiable event, the appraisal operator25 further makes the determination to refrain from presenting thesuspect event to the third phase of the sensing architecture 20, theclassification phase 23. Specifically, the appraisal operator 25prevents information from suspect event 26 from proceeding any furtherin the decision making process of the sensing architecture 20. As such,the waveform appraisal phase 25 greatly reduces the likelihood thatsuspect events will inappropriately direct treatment.

In further illustration, the appraisal operator 25 also confirmsaccurately sensed events. When an accurately sensed event is presentedto the appraisal operator 25 of the sensing architecture 20, it will becertified. After the appraisal operator 25 has confirmed that the sensedevent is certifiable, the appraisal operator 25 then presents the sensedevent to the classification phase 23 for its consideration. Thus, again,only sensed events that have been processed through the waveformappraisal phase 22 will be presented to the classification phase 23 ofthe sensing architecture 20. All suspect events are prevented 26 by theappraisal operator 25 from being available to the classification phase23.

The third and final phase of the sensing architecture 20 is theclassification phase 23. The classification phase 23 receives datacorresponding to events certified by the appraisal operator 25 andperforms certain mathematical operations to this certified data. Throughthese mathematical operations, the classification phase 23 examinesattributes such as rate, template comparisons and morphologicalcharacteristics, among others. Some illustrative classification phase 23operations are further discussed in U.S. patent application Ser. No.10/856,084 titled METHOD FOR DISCRIMINATING BETWEEN VENTRICULAR ANDSUPRAVENTRICULAR ARRHYTHMIAS, filed May 27, 2004, now U.S. Pat. No.7,330,757 and the disclosure of which is incorporated herein byreference. Any other suitable classification or analytical methods mayalso be used, as desired. These analyses aid the sensing architecture 20in determining whether the certified events are associated with aparticular class of rhythms. The classification phase 23 preferablyaccumulates a sufficient amount of data to render a determination whichdirects the cardiac rhythm management device to either withhold ordeliver therapy to a patient.

The incorporation of a waveform appraisal phase 22 into a sensingarchitecture 20 enables various technologies described herein to possessenhanced positive predictivity values. The mathematical formula forpositive predictivity is as follows:Positive Predictivity=(True Positives)/(True Positives+False Positives).

In several illustrative implementations, only certified events, andtherefore only the highest quality, accurate and representative data,are designed to be sent to the classification phase 23 for evaluation.As such, even legitimate cardiac signals possessing poor quality may notbe sent to the classification phase 23 for evaluation. The waveformappraisal phase 22, therefore, is designed to eliminate thepreponderance of false positives from consideration by theclassification phase. By reducing the number of false positives observedin a classification scheme, the positive predictivity increases and thesystem benefits from the reduction in inappropriate therapies deliveredto a patient.

This heightened positive predictivity is directly observable in countingschemes used within the classification phase 23 employed by cardiacrhythm management devices. For example, the sensing architecture 20 mayutilize an X out of Y parameter requiring the classification of eighteenmalignant cardiac events out of twenty-four total detected and certifiedevents to declare an episode. Various implementations can utilize thisclassic X out of Y filter; however, the Y input may only comprise thoseevents that have been certified. Suspect events, which will include thepreponderance of false positives, will have been rejected by theappraisal operator 25 and would not be included in the Y input.Similarly, the X input comprises only those events that are appraised asbeing certified events through the waveform appraisal phase 22 andclassified as dysrhythmic events through the classification phase 23.Thus, a preponderance of false positives are removed by varioustechnologies described herein, dramatically improving the system'spositive predictivity.

In contrast, the inclusion of false positive events in the X out of Yfilter will result in the reduction of positive predictivity. Therefore,in systems without a waveform appraisal phase 22, the positivepredictivity of the counting scheme may be compromised during lowquality electrocardiograms. If the positive predictivity is compromised,this may decreases the system's ability to accurately and reliablydirect therapy to a patient.

FIG. 3 shows an approximately nine second segment of a patient'selectrocardiogram 30 that is of low quality. Referring both to FIGS. 2and 3, the electrocardiogram in FIG. 3 was processed through the sensingarchitecture 20; including the waveform appraisal phase 22 of theillustrative implementation. The electrocardiogram 30 shows sevencertified events (depicted by the symbol of an inverted triangle) andten suspect events that were attributed as possessing low quality(depicted by the symbol of a dotted upright triangle). In thisparticular example, the low quality of the electrocardiogram isattributable to muscle artifact noise.

The first five sensed events 32 in the electrocardiogram were sensed bythe detection phase 21, certified through the waveform appraisal phase22, and presented to the classification phase 23 of the sensingarchitecture 20 as true cardiac complexes. In contrast, the tensubsequently following sensed events 34 in time were sensed by thedetection phase 21, and evaluated and rejected through the waveformappraisal phase 22 as being suspect. Thus, these ten suspect events werenot presented to the classification phase 23—as noted illustratively bythe placement of a solid dot in an upright triangle. The last two sensedevents 36 in time in the electrocardiogram, however, are depicted asbeing sensed and certified, and were presented to the classificationphase 23.

If the overall system makes use of a counter or register to determinewhen to provide therapy to a patient, the occurrence of suspect events34-34 need not necessarily reset or undermine the counting scheme to anygreat extent. In the illustrative example, counting during a low-qualitysignal is suspended during the occurrence of one, or a series of suspectevents—such as those sensed events 34 graphically illustrated in FIG. 3.Implementations of the classification phase 23 of various technologiesdescribed herein could suspend the count through the low quality signaldetection, and again continue the count where it left off following thepassage of the low quality signal detection. Thus, in the abovedescribed example, the classification phase 23 would detect thenon-continuity of the stream of sensed data, but could still attributethe first certified event following the interruption, the count ofeleven and not one. This feature permits the sensing architecture 20 togreatly reduce any delay in detection. More specifically, the countingrequirement could be fulfilled more quickly by the ability of varioustechnologies described herein to hold a count as non-certifiable(suspect) events are rejected by the waveform appraisal phase 22, andtherefore, would be quicker to declare an episode than a prior artdevice that must restart the count following the detection of noise.Thus, various technologies described herein are capable of swift andaccurate episode detection, which significantly increases the success oftherapy delivered to a patient.

Certain implementations of various technologies described herein andcounting operations may also limit the ability to suspend a count. Forexample, it would be less desirable to have a counting operation,requiring a preset number of events before declaring an episode, be oneevent shy of the required number, experience a considerable low-qualitysignal detection period, and then declare an episode on the firstcertified event following the low-quality signal detection. In this lastexample, various technologies described herein may hold the count outlonger to assure that the most recently sensed events are part of thetrend observed prior to the low-quality signal detection. Similarly, ifa lengthy low-quality signal is observed by some implementations (onethat far outnumbers the previously certified events) or the continuityof the sensed signal is extremely poor, those implementations could alsorestart a count to assure that the declaration is accurate.

FIGS. 4 and 5 show how the application of various implementations canenhance ICD operation in directing therapy to a patient. The ratethreshold for arrhythmia declaration in both FIGS. 4 and 5 isapproximately 180 BPM, and is depicted as a solid line 48. The runningaverage cardiac rate is depicted generally as line 47.

The electrocardiogram in FIG. 4 illustrates a scenario where thecalculated rate is the only determinative factor in deciding whether toapply or withhold therapy. Therefore, the analytical method applied tothe electrocardiogram in FIG. 4 does not include a waveform appraisalphase. In the electrocardiogram of FIG. 4, a normal sinus rhythminterspersed with low quality cardiac events is depicted as segment 40,and a high quality segment of normal sinus rhythm is shown as segment42.

The upward excursions of the running cardiac rate 47 during segment 40are caused by the inappropriate counting of low-quality events. As aresult of this inappropriate rate counting, the patient would have beendelivered at least one inappropriate shock, because the onlydeterminative factor for therapy is rate. The points in theelectrocardiogram where an event is declared using a prior art algorithmis shown as lines 44 and 46.

The electrocardiogram in FIG. 5 illustrates a scenario where a sensingarchitecture such as sensing architecture 20 in FIG. 2 is used,including a waveform appraisal phase 22, as discussed above withreference to FIG. 2. The inclusion of the waveform appraisal phase 22greatly reduces the instances of inappropriate rate counting, and,therefore, inappropriate shocks, such as the ones declared in FIG. 4.When the illustrative sensing architecture 20 evaluates the sameelectrocardiogram signal as FIG. 4, it rejects the non-certifiableevents as suspect. After the waveform appraisal phase 22 rejects thesuspect events, it is observed that the illustrative implementation doesnot include those suspect events in calculating the running averagecardiac rate, and therefore does not deliver therapy. Specifically, whenthe appraisal operator 20 is presented with the low-quality segment 40,the waveform appraisal phase 22 evaluates the low-quality segment 40,and finds it to be of insufficient quality to use for declaring anevent. Thus, in striking comparison to an industry standard sensingarchitecture as used for the evaluation shown in FIG. 4, theillustrative implementation does not deliver therapy based on thelow-quality signals observed in segment 40.

In one implementation, whether a cardiac event is accurately detected bythe detection phase 21 of the sensing architecture 20, the detectionphase 21 is not adjusted by the waveform appraisal phase'sdeterminations. The detection phase 21 continues to operateindependently of the remaining portions of the sensing architecture 20.Thus, although the waveform appraisal phase 22 may be evaluatingdetected events as suspect beats, the detection phase 21 of the sensingarchitecture 20 continues to sense such events in its customary manner.In another implementation, the detection phase 21 may adjust its sensingparameters to compensate for the frequency and number ofmischaracterized, and therefore, suspect events.

Although various technologies described herein have been described withrelation to an ICD, a pacing device, such as a pacemaker, may utilizethese various technologies when in an ATP state. Thus, when a pacemakeris pacing a heart out of a tachyarrhythmia, the pacemaker may utilizethe multi-phase sensing architecture of various technologies describedherein to certify whether sensed events have high quality or whetherthey are of low quality such that they may cause a mischaracterizeddetection. Additionally, there are other cardiac rhythm managementdevices that may have applicable states where the sensing architectureis particularly suited and beneficial.

Referring again to FIG. 2, the sensing architecture 20 may be capable ofimplementing several appraisal operators 25, and mechanisms necessaryfor the performance of the waveform appraisal phase 22. As describedabove, the events sensed are highly dependent on the type of cardiacrhythm management device used. Likewise, the appraisal operator 25, andthe mechanics behinds its operation, is highly dependent on both thecardiac rhythm management device used and the type of events sensed andrequiring certification. Various technologies described herein,therefore, are not limited in terms of the particular mechanics usedduring the waveform appraisal phase of the sensing architecture 20. Thefollowing descriptions are to illustrate an exemplary mode orconfiguration chosen from numerous plausible examples.

FIG. 6 shows a block diagram illustrating the steps employed in someimplementations of various technologies described herein for waveformappraisal. From a start block 50, the waveform appraisal is triggeredwhen an event is sensed, as noted at 52. Next, characteristic featuresof the sensed event are observed, as shown at 54. As noted, the“characteristic features” may take many forms. In one implementation,the characteristic features concern the shape of the sensed event. Somecharacteristic features that relate to the event's shape include theinclusion of monotonic segments, monotonic sample groups, or significantmaximum slope points (example methods incorporating each are shown,respectively, in FIGS. 7, 8, and 12). Those skilled in the art willrecognize that many of the characteristic features provided havesuitable alternatives that may be utilized.

The waveform appraisal method in FIG. 6 continues with the step ofcounting the characteristic features, as shown at 56. The number ofcharacteristic features is then compared to a threshold, as noted at 58.If the threshold is met, the event is certified as shown at 60, and thewaveform appraisal is complete 62. The system then submits the certifiedevent to the classification phase for further analysis. If the thresholdis not met, the event is found to be a suspect event that is unsuitablefor further analysis, as shown at 64. Then, the system is directed toreturn to the event sensing module or step until a next event is sensed,as shown at 66.

FIG. 7 shows a block diagram illustrating the steps employed in anotherimplementation of various technologies described herein for waveformappraisal. From a start block 70, the system senses an event 72. Oncethe event is sensed 72, the system then implements the waveformappraisal phase, including at least some of steps 74-86. First, acollection of Z samples is taken from the sensed event, as shown at 74.This collection of Z samples is analyzed to count the monotonic groupstherein, as noted at 76.

The step of counting monotonic groups 76 may be performed, for example,by comparing each successive sample to its predecessor. First a valuefor a group counter (typically stored in a counter, register or othermemory location) is set to zero. Starting with a first sample, the nextsample is compared. If the second sample has a value that is greaterthan the first sample, a direction register can be set to indicate thatthe samples are increasing in amplitude with time; alternatively, if thesecond sample has a value that is less than the first sample, thedirection register may be set to indicate that the samples aredecreasing. If the second sample has the same amplitude as the firstsample, then the direction register may be left at its previous value(which is irrelevant until set). If desired, there may be a minimumchange in amplitude required to cause a change in the directionregister. Each successive sample is then compared in turn. Whenever thedirection register is set to a new value, indicating a change indirection of sensed amplitude change over time, the group counter isincremented to indicate that a new monotonic segment has started.

After the step of counting monotonic groups shown at 76, the number ofmonotonic groups is compared to a threshold Y, as noted at 78. If thereare less than Y monotonic groups in the Z samples, this indicates a highquality sensed event. A YES result 80 calls for certifying the event,and the system goes to an end 82 that directs the certified event to theclassification phase. If a NO result 84 occurs, the system rejects theset of Z samples as a suspect event, discarding the samples from memory,and returns to the sensing step as shown at 86.

FIG. 8 is a block diagram of another illustrative implementation of anappraisal system 80 including a waveform appraisal phase. The systembegins at start block 90 by the system detecting an event. Thisillustrative implementation is adapted to work with a sensingarchitecture that operates in terms of blocks of samples that arereceived and then sent forward in the analysis structure. As shown atstep 92, a set of Z samples are received by the system. The Z samplesare then divided into groups of n samples at shown at 94. Each group ofsamples is evaluated to determine whether it is monotonic or not, andthese groups are counted as shown at 96. The system next checks whetherat least a threshold value, Y, of the groups are monotonic, as shown at98. For example, given thirty-two samples, the system may divide the setinto eight groups of four samples and determine how many of the groupsare monotonic. For such an example, a value of Y=5 could be used, suchthat five or more of the groups of samples would have to be monotonic toindicate a certified event.

If there are at least Y monotonic groups, the event is certified as ahigh-quality sensed event, as shown at 100. The waveform appraisal phasethen ends and the system directs the certified event to theclassification phase, as shown at 102. Otherwise, if there are less thanY monotonic groups in the set of Z samples, the method rejects the setof samples as a suspect event, as shown at 104, and returns to thesensing block as shown at 106.

FIGS. 9A-9B show operation of an illustrative waveform appraisal systemon a sensed event. The sensed event 110, as shown in its continuous timeanalog representation in FIG. 9A, is rather idealized and includes onlythat portion of the cardiac signal including the QRS complex. The Twave, in particular, has been excluded by keeping the time window ofsensing narrow. For example, the time window of sensing may be less thanone second, less than six-hundred milliseconds, or in the range of aboutfifty to two-hundred-fifty milliseconds. FIG. 9B illustrates a sampled,discrete time representation of the sensed event 110, with the signalincluding thirty-two samples. The representation of FIG. 9B, as can beappreciated by looking at FIG. 9A, is a temporally ordered set ofsamples. The numbers illustrate the number of monotonic segments andwhen they start by a method similar to the method of FIG. 7. Thebrackets with Y and N letters placed above illustrate whether groupedsamples are monotonic by the method of FIG. 8, with the thirty-twosamples placed in groups of four.

As can be seen, the event in FIG. 9B includes six monotonic segments ascounted in the manner illustrated in FIG. 7. If a further refinement isincluded where a segment illustrating no change is not considered aseparate monotonic segment, segments 1-2 and 5-6 would each count as asingle monotonic segment such that the beat would have only fourmonotonic segments. If a maximum number of segments is set at six, thenthe sensed event 110 of FIGS. 9A-9B would be certified.

For the method of FIG. 8, the results of the group checks yields sixmonotonic groups and two groups that are not monotonic. If a thresholdof 5/8 groups being monotonic is used, then the sensed event 110 ofFIGS. 9A-9B would be certified.

FIGS. 10A-10B show operation of an illustrative appraisal operation on asensed event 120, however, in this example, the sensed event 120 is alow quality event that does not resemble a typical cardiac event. Again,FIG. 10A illustrates the sensed event 120 in continuous, analog form.FIG. 10B is a sampled (and, if desired, digitized), temporally orderedform of the event, and again indicates analytical results with numbers,brackets, and letters. Using the method of FIG. 7, the sensed event 120includes sixteen monotonic segments. Again using the threshold of six,this sensed event 120 fails to meet the threshold and would beconsidered suspect. As a result, the method of FIG. 7 would not certifythe sensed event 120.

Applying the method of FIG. 8, the sensed event 120 has four groups thatare monotonic segments. Again, using a threshold of five monotonicsegments to be certifiable, the sensed event 120 would be found suspect.Application of the method of FIG. 7 would, again, not certify the sensedevent 120.

In another implementation, various techniques described herein mayinclude a method of waveform appraisal that includes counting certainmaximum slope points in a cardiac signal. The purpose of the maximumslope counter is to capture slope variation in the signal during thegenerated set of data. Low quality signals tend to have much more firstderivative variation than a clean high quality cardiac signal. Tocapture the variation of the first derivative, the second derivative ofthe generated set is computed and checked for zero crossings. For anillustrative implementation, a zero crossing of the second derivative isdefined as one where the second derivative crosses from a non-zeronegative to a non-zero positive value and vice versa. Preferably, simplyreaching zero is not considered a zero crossing point. Zero crossings ofthe second derivative of a single generally correspond to the points oflocal maximum slope (either positive or negative) of the originalsignal.

For the illustrative implementation, the first second derivative zerocrossing is accepted as a significant maximum slope point. After that,as each maximum slope point is encountered, it is checked to see if itis significant by applying two rules based on path length. The pathlength is defined as an accumulation of the magnitude of amplitudechanges in the original signal. The rules for the illustrativeimplementation are as follows:

-   -   1. The path length of the signal between the last significant        maximum slope point and the current maximum slope point must be        greater than the amplitude difference between the two points.    -   2. The path length of the signal between the last significant        maximum slope point and the current maximum slope point must be        greater than a programmed threshold value, derived as a        percentage (50%) of the average peak amplitudes of the beats        recorded prior to the current detection. If desired, a maximum        or minimum for the threshold value may be set.

In an illustrative example, using an 8 bit ADC, if the derived thresholdvalue is less than 7 ADC units, the threshold is set at 7 ADC units. Ifthe derived threshold value is greater than 20 ADC units, the thresholdis set at 20 ADC units.

FIG. 11 illustrates a method of signal analysis for counting significantmaximum slope points. In the illustrative example, a number of signalsample points are shown along with a corresponding analog signal 130.The method includes determining where the second derivative of thesampled signal crosses zero, indicating a maximum magnitude for theslope of the signal at each point. Points A, B, C, D, E and F indicatethese points.

Next, the method includes the step of determining which of points A-Fare significant for the purpose of appraising the signal. The magnitudeof amplitude change from point to point is determined, including themagnitude of such changes for the intermediate points between pointsA-F. These amplitude changes are indicated as segments Δ0-Δ7 in FIG. 11.A path length value is then determined. The path length value, as notedabove, is the accumulation of the magnitude of amplitude changes in thesampled signal occurring between two points. Thus, the sum of themagnitudes of segments Δ0 to Δ2 is the path length from C to D, the sumof the magnitudes of segments Δ0 to Δ7 is the path length from C to E,and the sum of the magnitudes of segments Δ3 to Δ7 is the path lengthfrom D to E.

Next, the actual change in signal amplitude between the points ismeasured. With these values, the two rules noted above are applied todetermine which maximum slope points are significant for appraising thesignal. For illustrative purposes, point C is assumed to be certified (Cwould in fact be certified in the Figure). Using point C as a referencepoint, from point C to point D, the path length is the same as theamplitude change, therefore, point D is not a significant maximum slopepoint under Rule 1. Therefore, point D is rejected.

The next step is to go to the next identified maximum slope point, pointE, to determine if it is significant for the appraisal method. In thiscase, the path length from point C to point E exceeds the amplitudechange between these two points. Rule 2 is passed because the pathlength exceeds the illustrated threshold path length minimum. Becausethe requirements of both rules are met, point E is a significant maximumslope point for the appraisal method.

In further illustration, it can be seen that point B is not asignificant maximum slope point because the path length from point A topoint B does not exceed the threshold path length minimum. This failsRule 1, above, and point B would be rejected.

The above analysis yields three significant maximum slope points in thesignal shown in FIG. 11. If, for example, the threshold maximum numberof significant maximum slope points is set at six, then the illustratedsignal is considered a certified signal.

FIG. 12 shows in block form steps of an illustrative maximum slope pointcounting method for appraising a received signal. The method begins byreceiving a signal, as shown at 140. Next, the maximum slope points areidentified, as noted at 142. The first maximum slope point is identifiedas a significant maximum slope point, as noted at 144. As illustrated inblock 146, the rest of the significant maximum slope points areidentified by taking a next maximum slope point as shown at 148, andapplying the first rule as shown at 150 and the second rule as shown at152. After the significant maximum slope points are identified in block148, the number of significant maximum slope points is compared to athreshold, as shown at 154. If the threshold is not exceeded, the signalis certified, as noted at 156. If the threshold is exceeded, then thesignal is marked as suspect, as noted at 158. In some implementations,after the signal is marked as suspect, it may be subjected to furtheranalysis, for example, to determine if changes in the event detectionarchitecture are needed. In other implementations, suspect signals arediscarded.

The following illustrative implementations are explained in terms ofoperational circuitry. The operational circuitry may be configured toinclude such controllers, microcontrollers, logic devices, memory, andthe like, as selected, needed, or desired, for performing the steps forwhich each is configured.

An illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry. For the illustrative implementation, the lead electrodeassembly is coupled to the canister; and the operational circuitry isconfigured to receive a cardiac signal from implanted electrodes,observe characteristic features of the shape of the signal, count thecharacteristic features, and compare the number of characteristicfeatures to a threshold. With the comparison to the threshold, theoperational circuitry is further configured to certify the signal foruse in characterizing a cardiac complex, or determine the signal isunsuitable for use in characterizing a cardiac complex. In anotherimplementation, the operational circuitry is configured such that thestep of receiving a signal includes sensing electrical activity andusing an event detector to determine the parameters of the signal. Inyet a further implementation, the operational circuitry is configuredsuch that the step of observing characteristic features includesidentifying a number of points in the signal where the signal slopereaches a local maximum magnitude.

For a related illustrative implementation, the implantablecardioverter/defibrillator includes operational circuitry configuredsuch that the step of identifying a number of points in the signalincludes identifying zero crossings of the second derivative of thesignal. In another implementation, the operational circuitry isconfigured such that the step of observing characteristic featuresincludes selecting a first zero crossing as a significant maximum slopepoint, and characterizing subsequent zero crossings as eithersignificant maximum slope points or not significant maximum slopepoints, wherein the significant maximum slope points are observed to bethe characteristic features. In a related implementation, theoperational circuitry is configured such that the step of characterizingsubsequent zero crossings includes application of a rule related to athreshold for consideration of separate points in the signal. In yetanother implementation, the operational circuitry is configured suchthat the rule calls for determining whether the path length from a mostrecent significant maximum slope point to the zero crossing underconsideration exceeds a path length threshold. A further relatedimplementation includes operational circuitry configured such that thepath length threshold is related to a selected percentage of the maximumsignal amplitude for a chosen cardiac complex.

In another implementation, the operational circuitry is configured suchthat the step of characterizing subsequent zero crossings includesapplication of a rule related to the signal shape between two points inthe signal. In a further, related implementation, the operationalcircuitry is configured such that the rule calls for determining whetherthe path length from a most recent significant maximum slope point tothe zero crossing under consideration exceeds the magnitude of thedifference in amplitude between the signal at the time of the mostrecent significant maximum slope point, and the signal at the time ofthe zero crossing under consideration.

In another implementation, the operational circuitry is configured suchthat a step of characterizing subsequent zero crossings includesanalysis using a first rule and a second rule, the first rule beingrelated to a threshold for consideration of separate points in thesignal, the second rule being related to the signal shape between twopoints in the signal. In a further implementation, the operationalcircuitry is configured such that the first rule calls for determiningwhether the path length from a most recent significant maximum slopepoint to the zero crossing under consideration exceeds a path lengththreshold, and the second rule calls for determining whether the pathlength from a most recent significant maximum slope point to the zerocrossing under consideration exceeds the magnitude of the difference inamplitude between, the signal at the time of the most recent significantmaximum slope point, and the signal at the time of the zero crossingunder consideration. In another related implementation, the operationalcircuitry is configured such that the step of observing characteristicfeatures of the signal includes assessment of the degree of monotonicityof the signal.

In another implementation, the operational circuitry is configured suchthat the step of observing characteristic features of the signalincludes counting a number of monotonic segments of the signal. Yetanother implementation includes operational circuitry configured suchthat the signal has a duration of less than one second. In severalimplementations, the implantable cardioverter/defibrillator includesoperational circuitry comprising a controller and a controller readablememory.

An illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to: sample asignal from an implanted electrode; identify maximum slope points in thesample signal corresponding to local signal slope maximums; analyze thesample signal to determine which of the maximum slope points aresignificant; and compare the number of significant maximum slope pointsto a threshold.

Another illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to: receive asignal from a pair of implanted electrodes; sense whether a likelycardiac event has occurred; observe the monotonic characteristics of thesignal during a time period corresponding to the likely cardiac event;and determine whether the signal during the time period is sufficientlymonotonic to indicate the cardiac signal can be certified forclassifying a cardiac rhythm. In a further implementation, theoperational circuitry is configured such that the step of observing themonotonic characteristics includes grouping together samples of thecardiac signal from the time period into sample groups, and observingwhether each sample group is monotonic. In yet another implementation,the operational circuitry is configured such that the step of observingthe monotonic characteristics includes counting the number of times thatan ordered set of samples of the cardiac signal corresponding to thetime period changes its direction of amplitude change. Anotherillustrative implementation includes operational circuitry is configuredsuch that the step of observing the monotonic characteristics includescounting a number of monotonic segments found in a sampling of thecardiac signal corresponding to the time period. An illustrativeimplementation further includes operational circuitry configured toclassify the event to determine whether the event indicates treatment.In another implementation, the operational circuitry is furtherconfigured to determine whether a cardiac signal appears to indicate apatient's heart is beating at a rate above a rate threshold beforeperforming the receive, sense, observe, and determine steps. In yetanother implementation, the operational circuitry comprises a controllerand a controller readable memory.

An illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to: sample acardiac signal from a pair of implanted electrodes; define a number ofgroups of samples from a portion of the signal; determine how many ofthe groups of samples are monotonic; and, if the number of monotonicgroups does not exceed a threshold, discard the portion of the signal.

Another illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to determinewhether a heart is pumping at a rate exceeding a threshold, and, if so,perform the following steps: sample a cardiac signal from a pair ofimplanted electrodes; define a number of groups of samples from aportion of the signal; determine how many of the groups of samples aremonotonic; and if the number of monotonic groups does not exceed athreshold, discard the portion of the signal. In another implementation,the operational circuitry is further configured to classify the signalby analyzing non-discarded portions of the signal, wherein the step ofclassifying the signal includes classifying portions of the signal aseither indicating treatment or not indicating treatment. In yet anotherillustrative implementation, the operational circuitry is furtherconfigured such that the step of classifying the signal includes keepinga count of classified portions until a threshold number of portions areclassified; wherein, when the threshold number of portions areclassified, the method further includes determining whether to providetreatment based upon whether a predetermined number of classifiedportions indicate treatment.

Another illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to: sample asignal from first and second implanted electrodes; set a threshold forthe monotonicity of a sensed signal; determine whether the sampledsignal meets the threshold; and if the sampled signal does not meet thethreshold, discard the signal.

Yet another illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to: receive asignal from first and second implanted electrodes; observe a point inthe signal; observe a number of samples of the signal following thepoint; group the samples into a number of groups; observe which of thegroups are monotonic; count the monotonic groups; determine whether thenumber of monotonic groups exceeds a threshold; and, if the number ofmonotonic groups does not exceed the threshold, discard the portion ofthe signal from which the number of signals following the point weretaken.

An illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to: receive asignal from first and second implanted electrodes; observe a monotonicsegment of the signal; measure the length of the monotonic segment;compare the length to a threshold; and, if the length is less than thethreshold, determine that the monotonic segment does not correspond to acertifiable cardiac event. In a related implementation, the operationalcircuitry is further configured to observe a portion of the signalaround the monotonic segment, determine whether the monotonic segmentends in a highest signal strength for the portion, and, if the monotonicsegment does not end in a highest signal strength for the portion,determining that the monotonic segment does not correspond to acertifiable cardiac event. In another implementation, the operationalcircuitry is further configured such that the portion corresponds totime window around the monotonic segment, at least a portion of the timewindow occurring before the monotonic segment begins, and at least aportion of the time window occurring after the monotonic segment ends,the window having a duration in the range of 50-250 milliseconds.

Another implementation includes an implantable cardiac treatment systemcomprising first and second electrodes, and circuitry contained in ahousing, the circuitry electrically coupled to the first and secondelectrodes, wherein the circuitry is adapted to perform the followingsteps: sampling a signal from first and second implanted electrodes;setting a threshold for the monotonicity of a sensed signal; determiningwhether the sampled signal meets the threshold; and, if the sampledsignal does not meet the threshold, discarding the signal. In a furtherimplementation, the circuitry includes a controller and a controllerreadable medium, the controller readable medium containing instructionsthereon for performing the steps of sampling, setting, determining anddiscarding.

Yet another implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to monitor asignal produced between implanted electrodes to observe an event,observe an event and gathering a signal corresponding to the event,observe characteristic features of the shape of the signal, count thecharacteristic features, and compare the number of characteristicfeatures to a threshold to certify the signal for use in characterizinga cardiac complex, or determine the signal is unsuitable for use incharacterizing a cardiac complex. In the illustrative implementation, ifthe signal is certified, the operational circuitry is also configured touse the signal to determine whether a malignant cardiac rhythm is likelyoccurring.

An illustrative implementation includes an implantablecardioverter/defibrillator comprising a lead electrode assemblyincluding a number of electrodes, and a canister housing operationalcircuitry, wherein the lead electrode assembly is coupled to thecanister, and the operational circuitry is configured to capture asignal using implanted electrodes placed for observing electricalcardiac activity, analyze the signal to determine whether the signal issuitable for characterizing a cardiac rhythm, and, if the signal issuitable, use the signal to determine whether a malignant cardiac rhythmis likely occurring, or, if the signal is not suitable, reject thesignal.

While the foregoing is directed to implementations of varioustechnologies described herein, other and further implementations may bedevised without departing from the basic scope thereof, which may bedetermined by the claims that follow. Although the subject matter hasbeen described in language specific to structural features and/ormethodological acts, it is to be understood that the subject matterdefined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims.

1. A method of identifying treatable cardiac conditions in animplantable cardiac stimulus device (ICSD), the ICSD comprising aplurality of electrodes for implantation in a patient and operationalcircuitry electrically coupled to the electrodes and configured toperform analysis of cardiac signals to identify malignant conditions,the method comprising: the ICSD monitoring a signal captured between theelectrodes; the ICSD detecting an event in the monitored signal; theICSD capturing a set of data associated with the detected event; theICSD dividing the captured set of data into a number of data subgroups;the ICSD assessing the shape of the signal in each of the datasubgroups; the ICSD characterizing the signal in each of the datasubgroups as either suggesting noise or not suggesting noise; the ICSDdetermining, for the captured set of data, how many of the datasubgroups suggest noise to generate a factor, N; the ICSD comparing N toa threshold for noise indications associated with a detected event; if Nexceeds the threshold for noise, the ICSD treating the detected event ascaused by noise; or else the ICSD treating the detected event as acardiac event.
 2. The method of claim 1 wherein: the set of datacomprises a series of A data samples; the step dividing the captured setof data into a number of data subgroups comprises dividing into sets ofB data samples; the step of assessing the shape of the signal in each ofthe data subgroups includes determining whether the each set of B datasamples is monophasic; and the step of characterizing the signal in eachof the data subgroups is performed such that any data subgroup which ismonophasic is found as not suggesting noise, and any data subgroup whichis not monophasic is found as suggesting noise.
 3. The method of claim 2wherein A is 32, B is 4, and the threshold for noise is
 5. 4. Animplantable cardiac stimulus device (ICSD), the ICSD comprising aplurality of electrodes for implantation in a patient and operationalcircuitry electrically coupled to the electrodes and configured toperform analysis of cardiac signals to identify malignant conditions,the operational circuitry being configured to perform a method ofidentifying cardiac event detections comprising: monitoring a signalcaptured between the electrodes; detecting an event in the monitoredsignal; capturing a set of data associated with the detected event;dividing the captured set of data into a number of data subgroups;assessing the shape of the signal in each of the data subgroups;characterizing the signal in each of the data subgroups as eithersuggesting noise or not suggesting noise; determining, for the capturedset of data, how many of the data subgroups suggest noise to generate afactor, N; comparing N to a threshold for noise indications associatedwith a detected event; if N exceeds the threshold for noise, treatingthe detected event as caused by noise; or else treating the detectedevent as a cardiac event.
 5. An ICSD as in claim 4 wherein theoperational circuitry is configured such that: the set of data comprisesa series of A data samples; the step dividing the captured set of datainto a number of data subgroups comprises dividing into sets of B datasamples; the step of assessing the shape of the signal in each of thedata subgroups includes determining whether the each set of B datasamples is monophasic; and the step of characterizing the signal in eachof the data subgroups is performed such that any data subgroup which ismonophasic is found as not suggesting noise, and any data subgroup whichis not monophasic is found as suggesting noise.
 6. The ICSD of claim 5wherein A is 32, B is 4, and the threshold for noise is 5.